Brain Computer Interfaces (BCI) seek to measure
brain signals in order to control computational or robotic
devices, with important applications to motor disability.
Electrocorticography (ECoG) is an emerging signal platform
for long term implantation of a brain signal recording device,
but current approaches rely heavily on screening tasks and
trained technicians to find and specify repeatable features in the
ECoG signal. Here we explore unsupervised approaches to
reducing the ECoG signal stream into a few components that
correspond most directly to neural patterns that correlate to
subject task performance (neural correlates). We report on the
development of a real-time feedback system we call the "Brain
Mirror" which is based on the real time, incremental learning
of a Deep Belief Network. On real patient data, we demonstrate
that the components learned online with Deep Belief Networks
have higher correlations with neural patterns than PCA